The invention relates to a method for matching an object model to a three-dimensional point cloud, wherein the point cloud is generated from two images by means of a stereo method, and a clustering method is applied to the point cloud in order to identify points respectively belonging to one cluster, and wherein model matching is subsequently carried out.
Object models are used to identify objects and to determine the three-dimensional location thereof. When matching an object model to a three-dimensional point cloud, known methods [Schmidt, Wöhler, C., Krüger, L., Gövert, T., Hermes, C., 2007. 3D Scene Segmentation and Object Tracking in Multiocular Image Sequences. Proc. Int. Conf on Computer Vision Systems (ICVS), Bielefeld, Germany] often result in ambiguity (false positive assignments). The object may be multiply found in the point cloud, even though it is not present that many times, or not present at all. A further problem relating to model matching is existing imprecision of the match. Currently conventional stereo methods are usually based on searching for features (edges, points, corners, blocks of pixels, etc.) in a left-hand and a right-hand image, and subsequently assigning identical/similar features to one another. Alternatively, it is often the case that the contents of local image windows are examined with regard to their similarity. The so-called disparity value is then determined by determining the offset of the assigned features or image windows in the left-hand and right-hand images with respect to one another. Assuming a calibrated camera system, a depth value can subsequently be assigned to the associated pixel from the disparity value by means of triangulation. False depth values occur in some cases as a result of a false assignment. In the case of edge-based stereo methods, this often occurs in repeating structures in the image, such as fingers on a hand, a forest, etc. The 3D points generated by the false assignment are referred to as false correspondences or outliers. Depending on the selection of features, this effect occurs more or less frequently, but can never be excluded generally without further assumptions. These false correspondences negatively influence the matching of the object model because they lead to a deterioration of the representation of the scene by the 3D point cloud.
The literature discloses various methods which deal with the problem of false correspondences. For the most part, the methods try to detect the outliers in order to subsequently eliminate them. A disadvantage in this case is the reduction in the number of 3D points, or the loss of information caused by this. Other methods [Hirschmuller, H, 2005. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information, Proc. IEEE Conf on Computer Vision and Pattern Recognition, San Diego, USA] try to suppress the problem, for example by assuming sectionwise smooth surfaces. As a result of such smoothness assumptions, fine structures can no longer be detected and this leads to a loss of information. Moreover, these methods only produce good results in cases where smooth surfaces can actually be expected.